Cloud Computing For Managers with Google Cloud

Discover how serverless options in Google Cloud enable managers to adopt cloud services, exploring BigQuery, Cloud Pub/Sub, Cloud Functions, and Cloud Firestore for data warehousing, event-driven processing, and NoSQL databases.

Explore serverless processing on Google Cloud, where BigQuery runs queries without provisioning machines. Learn how automatic scaling and transparent pay-per-data or time models simplify resource management.

BigQuery is a warehouse that decouples data and processing, uses data sets with schemas and SQL, and offers ten gigabytes of storage and one terabyte of processing free per month.

Explore how BigQuery behaves as a full standard SQL database, supporting select statements, insert and update operations, and constructs like group by, order by, having, and create table as select.

Explore BigQuery SQL through a hands-on demo, creating datasets and tables with ctas, running queries with where clauses and aliases, updating data, and viewing execution details and JSON results.

Learn to create external tables from Google Cloud Storage data in BigQuery, comparing console-based and SQL-based methods, handle schema issues, and drop external tables without losing data.

Explore how BigQuery time travel lets you view data as of system time up to seven days back, recover deleted or dropped data, and rely on backups for longer history.

Learn how partitioned tables in BigQuery speed up queries by storing data by date, time, integer, or ingestion time, using the underscore partition time for daily data.

Explore time travel and partitioning in BigQuery with partition date and underscore partition time for day-level data. Learn to query as of timestamp and alias partition columns to reveal history.

Discover Cloud Pub/Sub, a serverless, real-time messaging service that lets apps exchange events via topics with publishers and subscribers. Learn pull subscriptions, acks, and the basics of topic-based message routing.

Create topics and subscriptions in Google Cloud Pub/Sub, publish and pull messages, view messages and metrics, and connect with Data Flow and Cloud Functions while configuring optional schema or encryption.

Build loosely coupled event driven systems with an event bus, where publishers add data to channels and subscribers receive data immediately or from historic data stored in the bus.

Explore how cloud functions enable event-driven, pay-per-use compute on Google Cloud, triggered by storage and Pub/Sub events. Use Java, Python, or Go to build responsive, on-demand code with no provisioning.

Learn how to create and deploy a cloud function that reacts to cloud storage events, publishes messages to a sales topic, and tests with file uploads.

Set up a real-time no-sql document database on Google Cloud with Firestore by creating a region-specific instance, collections, and documents, enabling offline transactions and mobile access.

Join this demo to build a simple Python cloud function that processes topic messages, decodes base64 payloads, and stores data in a Cloud Firestore table, with end-to-end testing and troubleshooting.

Activate cloud functions and set up storage by selecting a data location, create a database, and review billing notifications as you prepare to use storage in a global function.

Demonstrates how to use Google Cloud Storage to upload documents, check for duplicates with Firestore and BigQuery, and log actions for reliable data handling.

Discover serverless computing with Google Cloud tools such as BigQuery, Pub/Sub, Cloud Functions, and Firestore for real-time data processing and decoupled storage, while preparing to optimize cloud storage costs.

Explore how organizations store and access massive data sets in the cloud, from gigabytes to petabytes. Learn about availability, security, backups, and faster access using Google Cloud storage.

Explore Google Cloud Storage: store data on the internet with buckets and objects, access from anywhere, pay only for use, with 10 gigabytes of storage monthly free, and multi-region availability.

Upload and download data to a bucket from the console or cloud shell, and manage folders by creating, deleting, renaming, moving, or copying files; also set public access.

Compressing large data reduces storage costs and network transfers before uploading. Google Cloud offers Google managed keys and customer managed keys; losing a key means losing encrypted data.

Identify common storage problems and learn how to prevent data loss with object versioning, retention policies, and lifecycle rules, and know when recovery is possible in Google Cloud.

Use lifecycle policies to move seldom accessed data to nearline, coldline, or archive storage, reducing costs with long minimum durations and supporting disaster recovery needs.

Compare json-based document formats, csv with quotes, and columnar formats, highlighting schemas, compression, and predicate pushdown to improve storage efficiency and query performance.

As a manager, implement organization-wide controls and audits to monitor Google Cloud storage costs, and define lifecycle policies that move data to cheaper storage and purge aged data.

Explore why cloud storage is popular and learn to create Google Cloud buckets, enable versioning and retention, encrypt data, and set lifecycle policies to cut costs and delete expired files.

Create a Google Cloud storage bucket, choose region or multi-region, set access and encryption, and apply lifecycle rules to transition data to archive and auto-delete after retention.

Demonstrates using the Google Cloud command line to manage storage—listing buckets and objects, uploading files, viewing creation times, and enabling object versioning to keep or remove versions.

Assess security considerations for storing organization data in the public cloud, covering data protection, costs, Google's responsibilities, and the steps you must take to ensure data security.

Master the shared responsibility model by recognizing Google Cloud handles authentication and protection, while you manage access with strong passwords, group permissions, and read only controls for sensitive data.

Identify who you are to access Google Cloud, using Gmail login or district IDs, with multi-factor authentication. Group users like the data scientist team to streamline permissions.

Discover how service accounts function as internal identities in Google Cloud for services like Compute Engine and App Engine. They simplify permission handling by tying access to specific services.

Define granular permissions in Google Cloud using the least privilege principle, enabling read and write access to storage, BigQuery, and Pub/Sub while restricting data modification.

Google Cloud bundles permissions into roles to reduce the 89 permissions an administrator would need, enabling predefined roles and custom roles for datasets, queries, and buckets.

Discover how Google Cloud treats resources as building blocks accessed via permissions, such as storage buckets and Compute Engine, organized in a hierarchy of organization, folders, and projects.

Identify primitive basic roles as overly broad; use predefined rules for common use cases and build custom roles by adding permissions, like BigQuery data viewer/editor and storage.

Protect personally identifiable information on Google Cloud Storage by encrypting data at rest and in transit, enforcing encrypted channels, and using dlp to scan and quarantine data.

Learn the shared responsibility model, where Google handles infrastructure security while you manage authorization, credentials, and access through IAM roles—basic, predefined, and custom—via the console.

Demonstrate Google Cloud security and identity and access management by building a custom data scientist role and configuring permissions on datasets, tables, and storage.

Discover monitoring, logging, and alerting best practices in Google Cloud to detect issues and data delays, with email or mobile alerts to keep you informed.

Explore Google site reliability engineering practices for building reliable, scalable systems with automated monitoring. Embrace 0.1 percent downtime to experiment toward 99.9 percent uptime.

Explore cloud operations for monitoring and alerts using metrics, logs, dashboards, and custom metrics, then set up alerts and incident management to automatically notify when issues occur.

Learn to define and monitor service levels using availability, durability, and key indicators; compare service level objectives and agreements, set proactive alerts, and weigh cost against reliability.

Learn how cloud logging works across Google Cloud services, including capturing, exporting, and rotating logs, using log viewer to create custom metrics, and set alerts on dashboard in hybrid environments.

Learn how to monitor with Google Cloud, create and query metrics, build dashboards and alerts, and manage incident responses with multi-channel notifications.

Use debugging, profiling, and monitoring tools to visualize flow, pinpoint issues, and inspect running applications; Google Cloud monitoring and free audit logs support cost considerations for logging and monitoring.

Automate logging to a common location and send emails only for downtime; monitor symptoms, not causes, and track latency and saturation as the four golden signals, addressing outliers.

Navigate the Google Cloud console to explore logs with log explorer, view cloud function executions, and identify errors by severity. Use BigQuery to run queries and download results.

Demonstrate creating a log-based metric to count duplicate messages and explore it with metrics explorer. Use Google Cloud Storage data to show data collection and per-minute aggregation.

Practice monitoring metrics across topics and services in cloud environments, using a one-minute window to view ratings and analyze messages.

Learn to build and customize dashboards in Google Cloud Monitoring with charts and time ranges. Practice adding metrics, auto refresh, and alerts to monitor apps, cloud storage, and BigQuery data.

Create and test alerting with notification channels in Google Cloud, configure incident thresholds, set up alert policies, and use the Google Cloud Console and mobile app to monitor incidents.

Learn best practices for monitoring cloud systems with logs, log-based metrics, dashboards, and alerts, and discover how to derive metrics from logs and receive smartphone alerts.

Explore miscellaneous tools on Google Cloud, including Compute Engine, App Engine, Kubernetes containers, Dataflow, Cloud Shell, Terraform, and Google Kubernetes Engine, to design scalable, multi-cloud infrastructure as code.

Explore serverless processing and learn to store data on Google Cloud Storage with lifecycle policies, while applying the shared responsibility model, managing security permissions, and configuring metrics and alerts.

Identify how cloud technology differs from traditional systems and apply decoupling, storage, compute, architecture, security, and monitoring to a global migration project using Google Cloud.

Discover how big data yields insights that drive better actions and prescriptive actions, enabled by cloud scalability that lets you set up big data solutions in minutes.

Adopt fully managed cloud services, pay only for data and processing, decouple data from processing with lifecycle policies, and automate with Google SRT and TerraForm Reform for infrastructure as code.

Analyze payment risks by migrating invoice and vendor payment data to Google Cloud Storage, transforming to Parquet with Dataflow, and visualizing insights in Tableau via BigQuery.

Decouple data from processing with Google Cloud Storage and Dataflow, transform to Parquet, perform sentiment analysis, and analyze results in BigQuery for management dashboards.

Decouple data from processing and export Salesforce data to a Google Cloud Storage bucket to fuel a product recommendation engine and visualize results in Data Studio.

Analyze log files in real time to improve data security by streaming on-premise Kafka data to Google Cloud, processed with Dataflow and stored in BigQuery, with anomaly detection and dashboards.

Apply Google Cloud tools through four case studies, decoupling data and compute, and design simple, cost-efficient storage to reduce cost and time using Dataflow, Cloud Pub/Sub, BigQuery, and BigQuery ML.